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Software (67)

By Ashley Ferguson

Thanks to the introduction of connected products, digital services, and increased customer expectations, it has been the trend for IoT enterprise spend to consistently increase. The global IoT market is projected to reach $1.4 trillion USD by 2027. The pressure to build IoT solutions and get a return on those investments has teams on a frantic search for IoT engineers to secure in-house IoT expertise. However, due to the complexity of IoT solutions, finding this in a single engineer is a difficult or impossible proposition.

So how do you adjust your search for an IoT engineer? The first step is to acknowledge that IoT solution development requires the fusion of multiple disciplines. Even simple IoT applications require hardware and software engineering, knowledge of protocols and connectivity, web development skills, and analytics. Certainly, there are many engineers with IoT knowledge, but complete IoT solutions require a team of partners with diverse skills. This often requires utilizing external sources to supplement the expertise gaps.

THE ANATOMY OF AN IoT SOLUTION

IoT solutions provide enterprises with opportunities for innovation through new product offerings and cost savings through refined operations. An IoT solution is an integrated bundle of technologies that help users answer a question or solve a specific problem by receiving data from devices connected to the internet. One of the most common IoT use cases is asset tracking solutions for enterprises who want to monitor trucks, equipment, inventory, or other items with IoT. The anatomy of an asset tracking IoT solution includes the following:

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This is a simple asset tracking example. For more complex solutions including remote monitoring or predictive maintenance, enterprises must also consider installation, increased bandwidth, post-development support, and UX/UI for the design of the interface for customers or others who will use the solution. Enterprise IoT solutions require an ecosystem of partners, components, and tools to be brought to market successfully.

Consider the design of your desired connected solution. Do you know where you will need to augment skills and services?

If you are in the early stages of IoT concept development and at the center of a buy vs. build debate, it may be a worthwhile exercise to assess your existing team’s skills and how they correspond with the IoT solution you are trying to build.

IoT SKILLS ASSESSMENT

  • Hardware
  • Firmware
  • Connectivity
  • Programming
  • Cloud
  • Data Science
  • Presentation
  • Technical Support and Maintenance
  • Security
  • Organizational Alignment

MAKING TIME FOR IoT APPLICATION DEVELOPMENT

The time it will take your organization to build a solution is dependent on the complexity of the application. One way to estimate the time and cost of IoT application development is with Indeema’s IoT Cost Calculator. This tool can help roughly estimate the hours required and the cost associated with the IoT solution your team is interested in building. In MachNation’s independent comparison of the Losant Enterprise IoT Platform and Azure, it was determined that developers could build an IoT solution in 30 hours using Losant and in 74-94 hours using Microsoft Azure.

As you consider IoT application development, consider the makeup of your team. Is your team prepared to dedicate hours to the development of a new solution, or will it be a side project? Enterprise IT teams are often in place to maintain existing operating systems and to ensure networks are running smoothly. In the event that an IT team is tapped to even partially build an IoT solution, there is a great chance that the IT team will need to invite partners to build or provide part of the stack.

HOW THE IoT JOB GETS DONE

Successful enterprises recognize early on that some of these skills will need to be augmented through additional people, through an ecosystem, or with software. It will require more than one ‘IoT engineer’ for the job. According to the results of a McKinsey survey, “the preferences of IoT leaders suggest a greater willingness to draw capabilities from an ecosystem of technology partners, rather than rely on homegrown capabilities.”

IoT architecture alone is intricate. Losant, an IoT application enablement platform, is designed with many of the IoT-specific components already in place. Losant enables users to build applications in a low-to-no code environment and scale them up to millions of devices. Losant is one piece in the wider scope of an IoT solution. In order to build a complete solution, an enterprise needs hardware, software, connectivity, and integration. For those components, our team relies on additional partners from the IoT ecosystem.

The IoT ecosystem, also known as the IoT landscape, refers to the network of IoT suppliers (hardware, devices, software platforms, sensors, connectivity, software, systems integrators, data scientists, data analytics) whose combined services help enterprises create complete IoT solutions. At Losant, we’ve built an IoT ecosystem with reliable experienced partners. When IoT customers need custom hardware, connectivity, system integrators, dev shops, or other experts with proven IoT expertise, we can tap one of our partners to help in their areas of expertise.

SECURE, SCALABLE, SEAMLESS IoT

Creating secure, scalable, and seamless IoT solutions for your environment begins by starting small. Starting small gives your enterprise the ability to establish its ecosystem. Teams can begin with a small investment and apply learnings to subsequent projects. Many IoT success stories begin with enterprises setting out to solve one problem. The simple beginnings have enabled them to now reap the benefits of the data harvest in their environments.

Originally posted here.

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By GE Digital

“The End of Cloud Computing.” “The Edge Will Eat The cloud.” “Edge Computing—The End of Cloud Computing as We Know It.”  

Such headlines grab attention, but don’t necessarily reflect reality—especially in Industrial Internet of Things (IoT) deployments. To be sure, edge computing is rapidly emerging as a powerful force in turning industrial machines into intelligent machines, but to paraphrase Mark Twain: “The reports of the death of cloud are greatly exaggerated.” 

The Tipping Point: Edge Computing Hits Mainstream

We’ve all heard the stats—billions and billions of IoT devices, generating inconceivable amounts of big data volumes, with trillions and trillions of U.S. dollars to be invested in IoT over the next several years. Why? Because industrials have squeezed every ounce of productivity and efficiency out of operations over the past couple of decades, and are now looking to digital strategies to improve production, performance, and profit. 

The Industrial Internet of Things (IIoT) represents a world where human intelligence and machine intelligence—what GE Digital calls minds and machines—connect to deliver new value for industrial companies. 

In this new landscape, organizations use data, advanced analytics, and machine learning to drive digital industrial transformation. This can lead to reduced maintenance costs, improved asset utilization, and new business model innovations that further monetize industrial machines and the data they create. 

Despite the “cloud is dead” headlines, GE believes the cloud is still very important in delivering on the promise of IIoT, powering compute-intense workloads to manage massive amounts of data generated by machines. However, there’s no question that edge computing is quickly becoming a critical factor in the total IIoT equation.

“The End of Cloud Computing.” “The Edge Will Eat The cloud.” “Edge Computing—The End of Cloud Computing as We Know It.”  

Such headlines grab attention, but don’t necessarily reflect reality—especially in Industrial Internet of Things (IoT) deployments. To be sure, edge computing is rapidly emerging as a powerful force in turning industrial machines into intelligent machines, but to paraphrase Mark Twain: “The reports of the death of cloud are greatly exaggerated.”

The Tipping Point: Edge Computing Hits Mainstream

We’ve all heard the stats—billions and billions of IoT devices, generating inconceivable amounts of big data volumes, with trillions and trillions of U.S. dollars to be invested in IoT over the next several years. Why? Because industrials have squeezed every ounce of productivity and efficiency out of operations over the past couple of decades, and are now looking to digital strategies to improve production, performance, and profit. 

The Industrial Internet of Things (IIoT) represents a world where human intelligence and machine intelligence—what GE Digital calls minds and machines—connect to deliver new value for industrial companies. 

In this new landscape, organizations use data, advanced analytics, and machine learning to drive digital industrial transformation. This can lead to reduced maintenance costs, improved asset utilization, and new business model innovations that further monetize industrial machines and the data they create. 

Despite the “cloud is dead” headlines, GE believes the cloud is still very important in delivering on the promise of IIoT, powering compute-intense workloads to manage massive amounts of data generated by machines. However, there’s no question that edge computing is quickly becoming a critical factor in the total IIoT equation. 

What is edge computing? 

The “edge” of a network generally refers to technology located adjacent to the machine which you are analyzing or actuating, such as a gas turbine, a jet engine, or magnetic resonance (MR) scanner. 

Until recently, edge computing has been limited to collecting, aggregating, and forwarding data to the cloud. But what if instead of collecting data for transmission to the cloud, industrial companies could turn massive amounts of data into actionable intelligence, available right at the edge? Now they can. 

This is not just valuable to industrial organizations, but absolutely essential.

Edge computing vs. Cloud computing 

Cloud and edge are not at war … it’s not an either/or scenario. Think of your two hands. You go about your day using one or the other or both depending on the task. The same is true in Industrial Internet workloads. If the left hand is edge computing and the right hand is cloud computing, there will be times when the left hand is dominant for a given task, instances where the right hand is dominant, and some cases where both hands are needed together. 

Scenarios in which edge computing will take a leading position include things such as low latency, bandwidth, real-time/near real-time actuation, intermittent or no connectivity, etc. Scenarios where cloud will play a more prominent role include compute-heavy tasks, machine learning, digital twins, cross-plant control, etc. 

The point is you need both options working in tandem to provide design choices across edge to cloud that best meet business and operational goals.

Edge Computing and Cloud Computing: Balance in Action 

Let’s look at a couple of illustrations. In an industrial context, examples of intelligent edge machines abound—pumps, motors, sensors, blowout preventers and more benefit from the growing capabilities of edge computing for real-time analytics and actuation. 

Take locomotives. These modern 200 ton digital machines carry more than 200 sensors that can pump one billion instructions per second. Today, applications can not only collect data locally and respond to changes on that data, but they can also perform meaningful localized analytics. GE Transportation’s Evolution Series Tier 4 Locomotive uses on-board edge computing to analyze data and apply algorithms for running smarter and more efficiently. This improves operational costs, safety, and uptime. 

Sending all that data created by the locomotive to the cloud for processing, analyzing, and actuation isn’t useful, practical, or cost-effective. 

Now let’s switch gears (pun intended) and talk about another mode of transportation—trucking. Here’s an example where edge plays an important yet minor role, while cloud assumes a more dominant position. In this example, the company has 1,000 trucks under management. There are sensors on each truck tracking performance of the vehicle such as engine, transmission, electrical, battery, and more. 

But in this case, instead of real-time analytics and actuation on the machine (like our locomotive example), the data is being ingested, then stored and forwarded to the cloud where time series data and analytics are used to track performance of vehicle components. The fleet operator then leverages a fleet management solution for scheduled maintenance and cost analysis. This gives him or her insights such as the cost over time per part type, or the median costs over time, etc. The company can use this data to improve uptime of its vehicles, lower repair costs, and improve the safe operation of the vehicle.

What’s next in edge computing 

While edge computing isn’t a new concept, innovation is now beginning to deliver on the promise—unlocking untapped value from the data being created by machines. 

GE has been at the forefront of bridging minds and machines. Predix Platform supports a consistent execution environment across cloud and edge devices, helping industrials achieve new levels of performance, production, and profit.

Originally posted here.

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TinyML focuses on optimizing machine learning (ML) workloads so that they can be processed on microcontrollers no bigger than a grain of rice and consuming only milliwatts of power.

By Arm Blueprint staff
 

TinyML focuses on the optimization of machine learning (ML) workloads so that they can be processed on microcontrollers no bigger than a grain of rice and consuming only a few milliwatts of power.

TinyML gives tiny devices intelligence. We mean tiny in every sense of the word: as tiny as a grain of rice and consuming tiny amounts of power. Supported by Arm, Google, Qualcomm and others, tinyML has the potential to transform the Internet of Things (IoT), where billions of tiny devices, based on Arm chips, are already being used to provide greater insight and efficiency in sectors including consumer, medical, automotive and industrial.

Why target microcontrollers with tinyML?

Microcontrollers such as the Arm Cortex-M family are an ideal platform for ML because they’re already used everywhere. They perform real-time calculations quickly and efficiently, so they’re reliable and responsive, and because they use very little power, can be deployed in places where replacing the battery is difficult or inconvenient. Perhaps even more importantly, they’re cheap enough to be used just about anywhere. The market analyst IDC reports that 28.1 billion microcontrollers were sold in 2018, and forecasts that annual shipment volume will grow to 38.2 billion by 2023.

TinyML on microcontrollers gives us new techniques for analyzing and making sense of the massive amount of data generated by the IoT. In particular, deep learning methods can be used to process information and make sense of the data from sensors that do things like detect sounds, capture images, and track motion.

Advanced pattern recognition in a very compact format

Looking at the math involved in machine learning, data scientists found they could reduce complexity by making certain changes, such as replacing floating-point calculations with simple 8-bit operations. These changes created machine learning models that work much more efficiently and require far fewer processing and memory resources.

TinyML technology is evolving rapidly thanks to new technology and an engaged base of committed developers. Only a few years ago, we were celebrating our ability to run a speech-recognition model capable of waking the system if it detects certain words on a constrained Arm Cortex-M3 microcontroller using just 15 kilobytes (KB) of code and 22KB of data.

Since then, Arm has launched new machine learning (ML) processors, called the Ethos-U55 and Ethos-U65, a microNPU specifically designed to accelerate ML inference in embedded and IoT devices.

The Ethos-U55, combined with the AI-capable Cortex-M55 processor, will provide a significant uplift in ML performance and improvement in energy efficiency over the already impressive examples we are seeing today.

TinyML takes endpoint devices to the next level

The potential use cases of tinyML are almost unlimited. Developers are already working with tinyML to explore all sorts of new ideas: responsive traffic lights that change signaling to reduce congestion, industrial machines that can predict when they’ll need service, sensors that can monitor crops for the presence of damaging insects, in-store shelves that can request restocking when inventory gets low, healthcare monitors that track vitals while maintaining privacy. The list goes on.

TinyML can make endpoint devices more consistent and reliable, since there’s less need to rely on busy, crowded internet connections to send data back and forth to the cloud. Reducing or even eliminating interactions with the cloud has major benefits including reduced energy use, significantly reduced latency in processing data and security benefits, since data that doesn’t travel is far less exposed to attack. 

It’s worth nothing that these tinyML models, which perform inference on the microcontroller, aren’t intended to replace the more sophisticated inference that currently happens in the cloud. What they do instead is bring specific capabilities down from the cloud to the endpoint device. That way, developers can save cloud interactions for if and when they’re needed. 

TinyML also gives developers a powerful new set of tools for solving problems. ML makes it possible to detect complex events that rule-based systems struggle to identify, so endpoint AI devices can start contributing in new ways. Also, since ML makes it possible to control devices with words or gestures, instead of buttons or a smartphone, endpoint devices can be built more rugged and deployable in more challenging operating environments. 

TinyML gaining momentum with an expanding ecosystem

Industry players have been quick to recognize the value of tinyML and have moved rapidly to create a supportive ecosystem. Developers at every level, from enthusiastic hobbyists to experienced professionals, can now access tools that make it easy to get started. All that’s needed is a laptop, an open-source software library and a USB cable to connect the laptop to one of several inexpensive development boards priced as low as a few dollars.

In fact, at the start of 2021, Raspberry Pi released its very first microcontroller board, one of the most affordable development board available in the market at just $4. Named Raspberry Pi Pico, it’s powered by the RP2040 SoC, a surprisingly powerful dual Arm Cortex-M0+ processor. The RP2040 MCU is able to run TensorFlow Lite Micro and we’re expecting to see a wide range of ML use cases for this board over the coming months.

Arm is a strong proponent of tinyML because our microcontroller architectures are so central to the IoT, and because we see the potential of on-device inference. Arm’s collaboration with Google is making it even easier for developers to deploy endpoint machine learning in power-conscious environments.

The combination of Arm CMSIS-NN libraries with Google’s TensorFlow Lite Micro (TFLu) framework, allows data scientists and software developers to take advantage of Arm’s hardware optimizations without needing to become experts in embedded programming.

On top of this, Arm is investing in new tools derived from Keil MDK to help developers get from prototype to production when deploying ML applications.

TinyML would not be possible without a number of early influencers. Pete Warden, a “founding father” of tinyML and a technical lead of TensorFlow Lite Micro at Google,&nbspArm Innovator, Kwabena Agyeman, who developed OpenMV, a project dedicated to low-cost, extensible, Python-powered machine-vision modules that support machine learning algorithms, and Arm Innovator, Daniel Situnayake a founding tinyML engineer and developer from Edge Impulse, a company that offers a full tinyML pipeline that covers data collection, model training and model optimization. Also, Arm partners such as Cartesiam.ai, a company that offers NanoEdge AI, a tool that creates software models on the endpoint based on the sensor behavior observed in real conditions have been pushing the possibilities of tinyML to another level. 

Arm, is also a partner of the TinyML Foundation, an open community that coordinates meet-ups to help people connect, share ideas, and get involved. There are many localised tinyML meet-ups covering UK, Israel and Seattle to name a few, as well as a global series of tinyML Summits. For more information, visit the tinyML foundation website.

Originally posted here.

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Once again, I’m jumping up and down in excitement because I’m going to be hosting a panel discussion as part of a webinar series — Fast and Fearless: The Future of IoT Software Development — being held under the august auspices of IotCentral.io

At this event, the second of a four-part series, we will be focusing on “AI and IoT Innovation” (see also What the FAQ are AI, ANNs, ML, DL, and DNNs? and What the FAQ are the IoT, IIoT, IoHT, and AIoT?).

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Panel members Karl Fezer (upper left), Wei Xiao (upper right), Nikhil Bhaskaran (lower left), and Tina Shyuan (bottom right) (Click image to see a larger version)

As we all know, the IoT is transforming the software landscape. What used to be a relatively straightforward embedded software stack has been revolutionized by the IoT, with developers now having to juggle specialized workloads, security, artificial intelligence (AI) and machine learning (ML), real-time connectivity, managing devices that have been deployed into the field… the list goes on.

In this webinar — which will be held on Tuesday 29 June 2021 from 10:00 a.m. to 11:00 a.m. CDT — I will be joined by four industry luminaries to discuss how to juggle the additional complexities that machine learning adds to IoT development, why on-device machine learning is more important now than ever, and what the combination of AI and IoT looks like for developers in the future.

The luminaries in question (and whom I will be questioning) are Karl Fezer (AI Ecosystem Evangelist at Arm), Wei Xiao (Principal Engineer, Sr. Strategic Alliances Manager at Nvidia), Nikhil Bhaskaran (Founder of Shunya OS), and Tina Shyuan (Director of Product Marketing at Qeexo).

So, what say you? Dare I hope that we will have the pleasure of your company and that you will be able to join us to (a) tease your auditory input systems with our discussions and (b) join our question-and-answer free-for-all frensy at the end? If so, may I suggest that you Register Now before all of the good virtual seats are taken, metaphorically speaking, of course.

>> Clicke here to register

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WEBINAR SERIES:
 
Fast and Fearless - The Future of IoT Software Development
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SUMMARY

The IoT is transforming the software landscape. What was a relatively straightforward embedded software stack, has been revolutionized due to the IoT where developers juggle specialized workloads, security, machine learning, real-time connectivity, managing devices in the field - the list goes on.

How can our industry help developers prototype ‘fearlessly’ because the tools and platforms allow them to navigate varying IoT components? How can developers move to production quickly, capitalizing on innovation opportunities in emerging IoT markets? 

This webinar series will take you through the fundamental steps, tools and opportunities for simplifying IoT development. Each webinar will be a panel discussion with industry experts who will share their experience and development tips on the below topics.

 

Part One of Four: The IoT Software Developer Experience

Date: Tuesday, May 11, 2021

Webinar Recording Available Here
 

Part Two of Four: AI and IoT Innovation

Date: Tuesday, June 29, 2021

Time: 8:00 am PDT/ 3:00 pm UTC

Duration: 60 minutes

Click Here to Register for Part Two
 

Part Three of Four: Making the Most of IoT Connectivity

Date: Tuesday, September 28, 2021

Time: 8:00 am PDT/ 3:00 pm UTC

Duration: 60 minutes

Click Here to Register for Part Three
 

Part Four of Four: IoT Security Solidified and Simplified

Date: Tuesday, November 16, 2021

Time: 8:00 am PDT/ 3:00 pm UTC

Duration: 60 minutes

Click Here to Register for Part Four
 
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Happy Friday (or whatever day it is when you find yourself reading this). I’m currently bouncing off the walls in excitement because I’ve been invited to host a panel discussion as part of a webinar series — Fast and Fearless: The Future of IoT Software Development — being held under the august auspices of IoTCentral.io

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Panel members Joe Alderson (upper left), Pamela Cortez (upper right), Katherine Scott (lower left), and Ihor Dvoretskyi (bottom right)

At this event, the first of a 4-part series, we will be focusing on “The IoT Software Developer Experience.”

As we all know, the IoT is transforming the software landscape. What used to be a relatively straightforward embedded software stack has been revolutionized by the IoT, with developers now having to juggle specialized workloads, security, machine learning, real-time connectivity, managing devices that have been deployed into the field… the list goes on.

In this webinar — which will be held on Tuesday 11 May 2021 from 10:00 a.m. to 11:00 a.m. CDT — I will be joined by four industry luminaries to discuss the development challenges engineers are facing today, how the industry is helping to make IoT development easier, an overview of development processes (including cloud-based continuous integration (CI) workflows and low-code development), and what the future looks like for developers who are building for the IoT. 

The luminaries in question (and whom I will be questioning) are Joe Alderson (Director of Embedded Tools and User Experience at Arm), Pamela Cortez (IoT Developer Advocate and Sr. Program Manager at Microsoft Azure IoT), Katherine Scott, Developer Advocate at Open Robotics, and Ihor Dvoretskyi (Developer Advocate at Cloud Native Computing Foundation).

So, what say you? Dare I hope that we will have the pleasure of your company and that you will be able to join us to (a) tease your auditory input systems with our discussions and (b) join our question-and-answer free-for-all at the end?

Recording available:

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Well, this isn’t something I expected to be talking about today, but my chum Ben Cook just introduced me to something that looks rather cool.

Ben is the Founder and Director at Airspeed Electronics Ltd., which is an electronic design consultancy that’s based in the UK specializing in high-performance acoustic detection and tracking technology for counter-unmanned aircraft system (UAS) applications. The folks at Airspeed Electronics are currently developing a drone detection and tracking system called MANTIS, where this work is being funded through a research grant provided by the UK Ministry of Defence (which — before you make a nasty comment — is how they spell “Defense” in the UK).

MANTIS, which stands for “MAchine learNing acousTIc Surveillance,” is a system of distributed, intelligent acoustic sensors that use artificial intelligence (AI) for the detection, classification, and location estimation of UAS — such as drones — based on their acoustic signatures.

But that’s not what I wanted to talk to you about…

In his email to me, Ben spake as follows: “Have you heard of an embedded operating system called ‘Luos’ before? It’s a microservices software architecture, like docker but for use with microcontrollers. I have no affiliation, I just stumbled across this today and I’m thinking this could be very useful for some future projects. It looks really good for anything ‘modular-y,’ if you know what I mean…”

I do know what Ben means. I just meandered my way around the luos.io website, perused and pondered the documentation at docs.luos.io, and watched this video on YouTube (later today, I’m going to get the tattoo, buy the T-shirt, and see the stage play).

In a nutshell, Luos is a simple and lightweight open-source distributed operating system dedicated to embedded systems. It uses the concept of modularity to simplify the linking of components and chunks of application code together to form a single system image.

Consider a system like a robot that uses multiple microcontrollers to manage its various sensors, actuators, and motors. If each of these microcontrollers employs Luos technology, all of them can use any feature of any microcontroller in the system as if all of the features were located in the same component.

Now, I’m a hardware design engineer by trade, so the software side is a bit outside my bailiwick, but — even so — looking at the video above and scanning the documentation makes me sit up and say, “Wow, this looks really, really cool.”

I asked around a few of my embedded systems software developer friends, and no one had heard of Luos, but I have a feeling that this may be a tool that’s poised to make a big splash. All sorts of ideas are currently bouncing around my head, like the fact that the Tracealyzer tool from Percepio would make an ideal companion for the Luos OS (see also The 2021 Embedded Online Conference Approacheth).

How about you? Have you heard of Luos? If so, what are your thoughts? If not, and if you lean toward the software side of things, it would be great if you could take a look with your highly trained eye, see what you think, and report back to the rest of us in the comments below.

Originally posted HERE

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The world has seen the emergence of countless advanced technologies over the past few decades and among them, one of the most notable and impactful has been the Internet of Things. Often described as a critical part of the foundation of our technology-driven future, IoT has shaken up pretty much every industry on the face of Earth. For the better, of course. Anyway, this transformation brought on by IoT has made its way into the world of web and mobile apps development too, and understandably so. After all, today apps are not only the most omnipresent modern tools but are relied upon by millions and millions of people every single day. Suffice it to say that app development is vital to the cause of keeping the world running and IoT has only entered the scene to further improve things. But the question remains: How?

The Internet of Things is a dynamic technology that has, for starters, completely changed how users in the digital realm interact and engage with web applications as well as mobile applications. It not only enables companies to effortlessly deal with humongous volumes of data, but also ensures top-notch security, seamless communications, and so much more. After all, the Internet of Things market is not projected to touch $11 trillion in economic value by 2025 without reason. Studies have also found that the global investment in IoT could touch $15 trillion by 2025. Now, let’s explore some of its other contributions to app development in detail.

1. Smarter UIs: While it can be quite challenging to put together UIs that integrate modern technologies and still successfully tend to users’ expectations, IoT can help considerably in this regard. It is highly conducive to the development of effective UI and integrates relevant latest trends to further enhance users’ experiences while engaging with the app. Oh, and let’s not forget that it also enables A/B split tests to help developers identify which iteration of the app is best suited for success.
2. Cybersecurity: Of course, ensuring high levels of security with your apps is a top priority for everyone. IoT helps ameliorate this process by helping programmers to integrate the latest security measures and strategies. This includes modern identification and authorization methods to monitor the continued safety of all the data stored within the app.
3. Chatbots: Now, artificial intelligence and machine learning-driven chatbots can be further integrated with the Internet of Things to empower them with access to even more sources of data. This, then, allows them to answer customers' queries and issues in a much more proficient manner, which is critical to ensuring high levels of customer satisfaction in an increasingly competitive market.
4. Data collection and processing: Data collection and its processing are critical to the success of any app in the world today. With the Internet of Things, that ability is fortified since one now gains access to a wider set of sources for data. Furthermore, IoT also helps avoid any lags in the relay of this information, thus enabling it to be used in real-time.

There is not even a shred of doubt that avant-garde technologies such as the Internet of Things, artificial intelligence, machine learning, etc. have completely transformed the web and mobile app development process and for good! So, if you want to take advantage of IoT and other such technologies to build modern apps that are more secure, highly customer experience-focused, and enable the seamless collection of data among other things, all you have to do is find a qualified service provider for their development. Their expertise and knowledge will further fortify your product and thus, your customers’ experiences.

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By Bee Hayes-Thakore

The Android Ready SE Alliance, announced by Google on March 25th, paves the path for tamper resistant hardware backed security services. Kigen is bringing the first secure iSIM OS, along with our GSMA certified eSIM OS and personalization services to support fast adoption of emerging security services across smartphones, tablets, WearOS, Android Auto Embedded and Android TV.

Google has been advancing their investment in how tamper-resistant secure hardware modules can protect not only Android and its functionality, but also protect third-party apps and secure sensitive transactions. The latest android smartphone device features enable tamper-resistant key storage for Android Apps using StrongBox. StrongBox is an implementation of the hardware-backed Keystore that resides in a hardware security module.

To accelerate adoption of new Android use cases with stronger security, Google announced the formation of the Android Ready SE Alliance. Secure Element (SE) vendors are joining hands with Google to create a set of open-source, validated, and ready-to-use SE Applets. On March 25th, Google launched the General Availability (GA) version of StrongBox for SE.

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Hardware based security modules are becoming a mainstay of the mobile world. Juniper Research’s latest eSIM research, eSIMs: Sector Analysis, Emerging Opportunities & Market Forecasts 2021-2025, independently assessed eSIM adoption and demand in the consumer sector, industrial sector, and public sector, and predicts that the consumer sector will account for 94% of global eSIM installations by 2025. It anticipates that established adoption of eSIM frameworks from consumer device vendors such as Google, will accelerate the growth of eSIMs in consumer devices ahead of the industrial and public sectors.


Consumer sector will account for 94% of global eSIM installations by 2025

Juniper Research, 2021.

Expanding the secure architecture of trust to consumer wearables, smart TV and smart car

What’s more? A major development is that now this is not just for smartphones and tablets, but also applicable to WearOS, Android Auto Embedded and Android TV. These less traditional form factors have huge potential beyond being purely companion devices to smartphones or tablets. With the power, size and performance benefits offered by Kigen’s iSIM OS, OEMs and chipset vendors can consider the full scope of the vast Android ecosystem to deliver new services.

This means new secure services and innovations around:

🔐 Digital keys (car, home, office)

🛂 Mobile Driver’s License (mDL), National ID, ePassports

🏧 eMoney solutions (for example, Wallet)

How is Kigen supporting Google’s Android Ready SE Alliance?

The alliance was created to make discrete tamper resistant hardware backed security the lowest common denominator for the Android ecosystem. A major goal of this alliance is to enable a consistent, interoperable, and demonstrably secure applets across the Android ecosystem.

Kigen believes that enabling the broadest choice and interoperability is fundamental to the architecture of digital trust. Our secure, standards-compliant eSIM and iSIM OS, and secure personalization services are available to all chipset or device partners in the Android Ready SE Alliance to leverage the benefits of iSIM for customer-centric innovations for billions of Android users quickly.

Vincent Korstanje, CEO of Kigen

Kigen’s support for the Android Ready SE Alliance will allow our industry partners to easily leapfrog to the enhanced security and power efficiency benefits of iSIM technology or choose a seamless transition from embedded SIM so they can focus on their innovation.

We are delighted to partner with Kigen to further strengthen the security of Android through StrongBox via Secure Element (SE). We look forward to widespread adoption by our OEM partners and developers and the entire Android ecosystem.

Sudhi Herle, Director of Android Platform Security 

In the near term, the Google team is prioritizing and delivering the following Applets in conjunction with corresponding Android feature releases:

  • Mobile driver’s license and Identity Credentials
  • Digital car keys

Kigen brings the ability to bridge the physical embedded security hardware to a fully integrated form factor. Our Kigen standards-compliant eSIM OS (version 2.2. eUICC OS) is available to support chipsets and device makers now. This announcement is a start to what will bring a whole host of new and exciting trusted services offering better experience for users on Android.

Kigen’s eSIM (eUICC) OS brings

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The smallest operating system, allowing OEMs to select compact, cost-effective hardware to run it on.

Kigen OS offers the highest level of logical security when employed on any SIM form factor, including a secure enclave.

On top of Kigen OS, we have a broad portfolio of Java Card™ Applets to support your needs for the Android SE Ready Alliance.

Kigen’s Integrated SIM or iSIM (iUICC) OS further this advantage

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Integrated at the heart of the device and securely personalized, iSIM brings significant size and battery life benefits to cellular Iot devices. iSIM can act as a root of trust for payment, identity, and critical infrastructure applications

Kigen’s iSIM is flexible enough to support dual sim capability through a single profile or remote SIM provisioning mechanisms with the latter enabling out-of-the-box connectivity, secure and remote profile management.

For smartphones, set top boxes, android auto applications, auto car display, Chromecast or Google Assistant enabled devices, iSIM can offer significant benefits to incorporate Artificial intelligence at the edge.

Kigen’s secure personalization services to support fast adoption

SIM vendors have in-house capabilities for data generation but the eSIM and iSIM value chains redistribute many roles and responsibilities among new stakeholders for the personalization of operator credentials along different stages of production or over-the-air when devices are deployed.

Kigen can offer data generation as a service to vendors new to the ecosystem.

Partner with us to provide cellular chipset and module makers with the strongest security, performance for integrated SIM leading to accelerate these new use cases.

Security considerations for eSIM and iSIM enabled secure connected services

Designing a secure connected product requires considerable thought and planning and there really is no ‘one-size-fits-all’ solution. How security should be implemented draws upon a multitude of factors, including:

  • What data is being stored or transmitted between the device and other connected apps?
  • Are there regulatory requirements for the device? (i.e. PCI DSS, HIPAA, FDA, etc.)
  • What are the hardware or design limitations that will affect security implementation?
  • Will the devices be manufactured in a site accredited by all of the necessary industry bodies?
  • What is the expected lifespan of the device?

End-to-end ecosystem and services thinking needs to be a design consideration from the very early stage especially when considering the strain on battery consumption in devices such as wearables, smart watches and fitness devices as well as portable devices that are part of the connected consumer vehicles.

Originally posted here.

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By Sachin Kotasthane

In his book, 21 Lessons for the 21st Century, the historian Yuval Noah Harari highlights the complex challenges mankind will face on account of technological challenges intertwined with issues such as nationalism, religion, culture, and calamities. In the current industrial world hit by a worldwide pandemic, we see this complexity translate in technology, systems, organizations, and at the workplace.

While in my previous article, Humane IIoT, I discussed the people-centric strategies that enterprises need to adopt while onboarding IoT initiatives of industrial IoT in the workforce, in this article, I will share thoughts on how new-age technologies such as AI, ML, and big data, and of course, industrial IoT, can be used for effective management of complex workforce problems in a factory, thereby changing the way people work and interact, especially in this COVID-stricken world.

Workforce related problems in production can be categorized into:

  1. Time complexity
  2. Effort complexity
  3. Behavioral complexity

Problems categorized in either of the above have a significant impact on the workforce, resulting in a detrimental effect on the outcome—of the product or the organization. The complexity of these problems can be attributed to the fact that the workforce solutions to such issues cannot be found using just engineering or technology fixes as there is no single root-cause, rather, a combination of factors and scenarios. Let us, therefore, explore a few and seek probable workforce solutions.8829066088?profile=RESIZE_584x

Figure 1: Workforce Challenges and Proposed Strategies in Production

  1. Addressing Time Complexity

    Any workforce-related issue that has a detrimental effect on the operational time, due to contributing factors from different factory systems and processes, can be classified as a time complex problem.

    Though classical paper-based schedules, lists, and punch sheets have largely been replaced with IT-systems such as MES, APS, and SRM, the increasing demands for flexibility in manufacturing operations and trends such as batch-size-one, warrant the need for new methodologies to solve these complex problems.

    • Worker attendance

      Anyone who has experienced, at close quarters, a typical day in the life of a factory supervisor, will be conversant with the anxiety that comes just before the start of a production shift. Not knowing who will report absent, until just before the shift starts, is one complex issue every line manager would want to get addressed. While planned absenteeism can be handled to some degree, it is the last-minute sick or emergency-pager text messages, or the transport delays, that make the planning of daily production complex.

      What if there were a solution to get the count that is almost close to the confirmed hands for the shift, an hour or half, at the least, in advance? It turns out that organizations are experimenting with a combination of GPS, RFID, and employee tracking that interacts with resource planning systems, trying to automate the shift planning activity.

      While some legal and privacy issues still need to be addressed, it would not be long before we see people being assigned to workplaces, even before they enter the factory floor.

      During this course of time, while making sure every line manager has accurate information about the confirmed hands for the shift, it is also equally important that health and well-being of employees is monitored during this pandemic time. Use of technologies such as radar, millimeter wave sensors, etc., would ensure the live tracking of workers around the shop-floor and make sure that social distancing norms are well-observed.

    • Resource mapping

      While resource skill-mapping and certification are mostly HR function prerogatives, not having the right resource at the workstation during exigencies such as absenteeism or extra workload is a complex problem. Precious time is lost in locating such resources, or worst still, millions spent in overtime.

      What if there were a tool that analyzed the current workload for a resource with the identified skillset code(s) and gave an accurate estimate of the resource’s availability? This could further be used by shop managers to plan manpower for a shift, keeping them as lean as possible.

      Today, IT teams of OEMs are seen working with software vendors to build such analytical tools that consume data from disparate systems—such as production work orders from MES and swiping details from time systems—to create real-time job profiles. These results are fed to the HR systems to give managers the insights needed to make resource decisions within minutes.

  2. Addressing Effort Complexity

    Just as time complexities result in increased  production time, problems in this category result in an increase in effort by the workforce to complete the same quantity of work. As the effort required is proportionate to the fatigue and long-term well-being of the workforce, seeking workforce solutions to reduce effort would be appreciated. Complexity arises when organizations try to create a method out-of-madness from a variety of factors such as changing workforce profiles, production sequences, logistical and process constraints, and demand fluctuations.

    Thankfully, solutions for this category of problems can be found in new technologies that augment existing systems to get insights and predictions, the results of which can reduce the efforts, thereby channelizing it more productively. Add to this, the demand fluctuations in the current pandemic, having a real-time operational visibility, coupled with advanced analytics, will ensure meeting shift production targets.

    • Intelligent exoskeletons

      Exoskeletons, as we know, are powered bodysuits designed to safeguard and support the user in performing tasks, while increasing overall human efficiency to do the respective tasks. These are deployed in strain-inducing postures or to lift objects that would otherwise be tiring after a few repetitions. Exoskeletons are the new-age answer to reducing user fatigue in areas requiring human skill and dexterity, which otherwise would require a complex robot and cost a bomb.

      However, the complexity that mars exoskeleton users is making the same suit adaptable for a variety of postures, user body types, and jobs at the same workstation. It would help if the exoskeleton could sense the user, set the posture, and adapt itself to the next operation automatically.

      Taking a leaf out of Marvel’s Iron Man, who uses a suit that complements his posture that is controlled by JARVIS, manufacturers can now hope to create intelligent exoskeletons that are always connected to factory systems and user profiles. These suits will adapt and respond to assistive needs, without the need for any intervention, thereby freeing its user to work and focus completely on the main job at hand.

      Given the ongoing COVID situation, it would make the life of workers and the management safe if these suits are equipped with sensors and technologies such as radar/millimeter wave to help observe social distancing, body-temperature measuring, etc.

    • Highlighting likely deviations

      The world over, quality teams on factory floors work with checklists that the quality inspector verifies for every product that comes at the inspection station. While this repetitive task is best suited for robots, when humans execute such repetitive tasks, especially those that involve using visual, audio, touch, and olfactory senses, mistakes and misses are bound to occur. This results in costly reworks and recalls.

      Manufacturers have tried to address this complexity by carrying out rotation of manpower. But this, too, has met with limited success, given the available manpower and ever-increasing workloads.

      Fortunately, predictive quality integrated with feed-forwards techniques and some smart tracking with visuals can be used to highlight the area or zone on the product that is prone to quality slips based on data captured from previous operations. The inspector can then be guided to pay more attention to these areas in the checklist.

  3. Addressing Behavioral Complexity

    Problems of this category usually manifest as a quality issue, but the root cause can often be traced to the workforce behavior or profile. Traditionally, organizations have addressed such problems through experienced supervisors, who as people managers were expected to read these signs, anticipate and align the manpower.

    However, with constantly changing manpower and product variants, these are now complex new-age problems requiring new-age solutions.

    • Heat-mapping workload

      Time and motion studies at the workplace map the user movements around the machine with the time each activity takes for completion, matching the available cycle-time, either by work distribution or by increasing the manpower at that station. Time-consuming and cumbersome as it is, the complexity increases when workload balancing is to be done for teams working on a single product at the workstation. Movements of multiple resources during different sequences are difficult to track, and the different users cannot be expected to follow the same footsteps every time.

      Solving this issue needs a solution that will monitor human motion unobtrusively, link those to the product work content at the workstation, generate recommendations to balance the workload and even out the ‘congestion.’ New industrial applications such as short-range radar and visual feeds can be used to create heat maps of the workforce as they work on the product. This can be superimposed on the digital twin of the process to identify the zone where there is ‘congestion.’ This can be fed to the line-planning function to implement corrective measures such as work distribution or partial outsourcing of the operation.

    • Aging workforce (loss of tribal knowledge)

      With new technology coming to the shop-floor, skills of the current workforce get outdated quickly. Also, with any new hire comes the critical task of training and knowledge sharing from experienced hands. As organizations already face a shortage of manpower, releasing more hands to impart training to a larger workforce audience, possibly at different locations, becomes an even more daunting task.

      Fully realizing the difficulties and reluctance to document, organizations are increasingly adopting AR-based workforce trainings that map to relevant learning and memory needs. These AR solutions capture the minutest of the actions executed by the expert on the shop-floor and can be played back by the novice in-situ as a step-by-step guide. Such tools simplify the knowledge transfer process and also increase worker productivity while reducing costs.

      Further, in extraordinary situations such  as the one we face at present, technologies such as AR offer solutions for effective and personalized support to field personnel, without the need to fly in specialists at multiple sites. This helps keep them safe, and accessible, still.

Key takeaways and Actionable Insights

The shape of the future workforce will be the result of complex, changing, and competing forces. Technology, globalization, demographics, social values, and the changing personal expectations of the workforce will continue to transform and disrupt the way businesses operate, increasing the complexity and radically changing where, and when of future workforce, and how work is done. While the need to constantly reskill and upskill the workforce will be humongous, using new-age techniques and technologies to enhance the effectiveness and efficiency of the existing workforce will come to the spotlight.

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Figure 2: The Future IIoT Workforce

Organizations will increasingly be required to:

  1. Deploy data farming to dive deep and extract vast amounts of information and process insights embedded in production systems. Tapping into large reservoirs of ‘tribal knowledge’ and digitizing it for ingestion to data lakes is another task that organizations will have to consider.
  2. Augment existing operations systems such as SCADA, DCS, MES, CMMS with new technology digital platforms, AI, AR/VR, big data, and machine learning to underpin and grow the world of work. While there will be no dearth of resources in one or more of the new technologies, organizations will need to ‘acqui-hire’ talent and intellectual property using a specialist, to integrate with existing systems and gain meaningful actionable insights.
  3. Address privacy and data security concerns of the workforce, through the smart use of technologies such as radar and video feeds.

Nonetheless, digital enablement will need to be optimally used to tackle the new normal that the COVID pandemic has set forth in manufacturing—fluctuating demands, modular and flexible assembly lines, reduced workforce, etc.

Originally posted here.

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In my last post, I explored how OTA updates are typically performed using Amazon Web Services and FreeRTOS. OTA updates are critically important to developers with connected devices. In today’s post, we are going to explore several best practices developers should keep in mind with implementing their OTA solution. Most of these will be generic although I will point out a few AWS specific best practices.

Best Practice #1 – Name your S3 bucket with afr-ota

There is a little trick with creating S3 buckets that I was completely oblivious to for a long time. Thankfully when I checked in with some colleagues about it, they also had not been aware of it so I’m not sure how long this has been supported but it can help an embedded developer from having to wade through too many AWS policies and simplify the process a little bit.

Anyone who has attempted to create an OTA Update with AWS and FreeRTOS knows that you have to setup several permissions to allow an OTA Update Job to access the S3 bucket. Well if you name your S3 bucket so that it begins with “afr-ota”, then the S3 bucket will automatically have the AWS managed policy AmazonFreeRTOSOTAUpdate attached to it. (See Create an OTA Update service role for more details). It’s a small help, but a good best practice worth knowing.

Best Practice #2 – Encrypt your firmware updates

Embedded software must be one of the most expensive things to develop that mankind has ever invented! It’s time consuming to create and test and can consume a large percentage of the development budget. Software though also drives most features in a product and can dramatically different a product. That software is intellectual property that is worth protecting through encryption.

Encrypting a firmware image provides several benefits. First, it can convert your firmware binary into a form that seems random or meaningless. This is desired because a developer shouldn’t want their binary image to be easily studied, investigated or reverse engineered. This makes it harder for someone to steal intellectual property and more difficult to understand for someone who may be interested in attacking the system. Second, encrypting the image means that the sender must have a key or credential of some sort that matches the device that will decrypt the image. This can be looked at a simple source for helping to authenticate the source, although more should be done than just encryption to fully authenticate and verify integrity such as signing the image.

Best Practice #3 – Do not support firmware rollbacks

There is often a debate as to whether firmware rollbacks should be supported in a system or not. My recommendation for a best practice is that firmware rollbacks be disabled. The argument for rollbacks is often that if something goes wrong with a firmware update then the user can rollback to an older version that was working. This seems like a good idea at first, but it can be a vulnerability source in a system. For example, let’s say that version 1.7 had a bug in the system that allowed remote attackers to access the system. A new firmware version, 1.8, fixes this flaw. A customer updates their firmware to version 1.8, but an attacker knows that if they can force the system back to 1.7, they can own the system. Firmware rollbacks seem like a convenient and good idea, in fact I’m sure in the past I used to recommend them as a best practice. However, in today’s connected world where we perform OTA updates, firmware rollbacks are a vulnerability so disable them to protect your users.

Best Practice #4 – Secure your bootloader

Updating firmware Over-the-Air requires several components to ensure that it is done securely and successfully. Often the focus is on getting the new image to the device and getting it decrypted. However, just like in traditional firmware updates, the bootloader is still a critical piece to the update process and in OTA updates, the bootloader can’t just be your traditional flavor but must be secure.

There are quite a few methods that can be used with the onboard bootloader, but no matter the method used, the bootloader must be secure. Secure bootloaders need to be capable of verifying the authenticity and integrity of the firmware before it is ever loaded. Some systems will use the application code to verify and install the firmware into a new application slot while others fully rely on the bootloader. In either case, the secure bootloader needs to be able to verify the authenticity and integrity of the firmware prior to accepting the new firmware image.

It’s also a good idea to ensure that the bootloader is built into a chain of trust and cannot be easily modified or updated. The secure bootloader is a critical component in a chain-of-trust that is necessary to keep a system secure.

Best Practice #5 – Build a Chain-of-Trust

A chain-of-trust is a sequence of events that occur while booting the device that ensures each link in the chain is trusted software. For example, I’ve been working with the Cypress PSoC 64 secure MCU’s recently and these parts come shipped from the factory with a hardware-based root-of-trust to authenticate that the MCU came from a secure source. That Root-of-Trust (RoT) is then transferred to a developer, who programs a secure bootloader and security policies onto the device. During the boot sequence, the RoT verifying the integrity and authenticity of the bootloader, which then verifies the integrity and authenticity of any second stage bootloader or software which then verifies the authenticity and integrity of the application. The application then verifies the authenticity and integrity of its data, keys, operational parameters and so on.

This sequence creates a Chain-Of-Trust which is needed and used by firmware OTA updates. When the new firmware request is made, the application must decrypt the image and verify that authenticity and integrity of the new firmware is intact. That new firmware can then only be used if the Chain-Of-Trust can successfully make its way through each link in the chain. The bottom line, a developer and the end user know that when the system boots successfully that the new firmware is legitimate. 

Conclusions

OTA updates are a critical infrastructure component to nearly every embedded IoT device. Sure, there are systems out there that once deployed will never update, however, those are probably a small percentage of systems. OTA updates are the go-to mechanism to update firmware in the field. We’ve examined several best practices that developers and companies should consider when they start to design their connected systems. In fact, the bonus best practice for today is that if you are building a connected device, make sure you explore your OTA update solution sooner rather than later. Otherwise, you may find that building that Chain-Of-Trust necessary in today’s deployments will be far more expensive and time consuming to implement.

Originally posted here.

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Image Source: SEGGER.com

Nearly every embedded software developer working in the IoT space is now building secure devices. Developers have been mostly focused on how to handle secure applications and the basic microcontroller technologies such as how to use Arms TrustZone or leverage multicore processors. A looming problem that many companies and teams are overlooking is that figuring out how to develop secure applications is just the first step. There are three stages to secure product lifecycle management and in today’s post, we will review what is involved in each stage.

As a quick overview, the stages, which can be seen in the diagram below, are:

  • Development
  • Test and Production Deployment
  • Maintenance and In-field Servicing

Let us look at each of these stages in a little more detail. 

Stage #1 – Development

Development is probably the area that most developers are the most familiar with, but at the same time, the area that they are learning to adapt to the most. Many developers have designed and built systems without ever having to take security into account. Development involves a lot more than just deciding which components to isolate and how to separate the software into secure and non-secure regions.

For example, during the development phase developers now need to learn how to develop in the environment where a secure bootloader is in place. They need to consider how to handle firmware fallbacks, if they are allowed and if so, under what conditions. Firmware images may need to be compressed on top of the need for authentication.

While the development stage has become more complicated, developers should not struggle too much to extrapolate their past experiences to developing secure firmware successfully.

Stage #2 – Test and Production Deployment

The area that developers will probably struggle with the most is the test and production deployment stage. Testing secure software requires additional steps to be taken that authenticate debug hardware so that the developer can access secure memory regions to test their code and successfully debug it. Even more importantly, care must be taken to install that secure software onto a product during production.

There are several ways this can be done, but one method is to use a secure flashing device like SEGGERS Flasher Secure. These devices can follow a multistep process that involves validating a user ID which allows the secure firmware to be installed on the device. The devices themselves limit how many and on what devices the firmware can be installed which helps to protect a team’s intellectual property and prevents unauthorized production of a product.

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Stage #3 – Maintenance and In-field Servicing

Finally, there is the maintenance and in-field servicing stage which is a partial continuation of the development phase. Once a product has been deployed into the field, it needs to be securely updated. Updates can be done manually in-field, or they can be done using an over-the-air update process. This involves a device being able to contact a secure firmware server that can compress and encrypt the image and transport it to the device. Once the device has received the image, it must decrypt, decompress and validate the contents of the image. If everything looks good, the image can then be loaded as the primary firmware for the device.

Conclusions

 There is much more to designing and deploying a secure device than simply developing a secure application. The entire process is broken up into three main stages that we have looked at in greater detail today. Unfortunately, we have only just scratched the surface!

Orignally posted here.

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In this blog, we’ll discuss how users of Edge Impulse and Nordic can actuate and stream classification results over BLE using Nordic’s UART Service (NUS). This makes it easy to integrate embedded machine learning into your next generation IoT applications. Seamless integration with nRF Cloud is also possible since nRF Cloud has native support for a BLE terminal. 

We’ve extended the Edge Impulse example functionality already available for the nRF52840 DK and nRF5340 DK by adding the abilities to actuate and stream classification outputs. The extended example is available for download on github, and offers a uniform experience on both hardware platforms. 

Using nRF Toolbox 

After following the instructions in the example’s readme, download the nRF Toolbox mobile application (available on both iOS and Android) and connect to the nRF52840 DK or the nRF5340 DK that will be discovered as “Edge Impulse”. Once connected, set up the interface as follows so that you can get information about the device, available sensors, and start/stop the inferencing process. Save the preset configuration so that you can load it again for future use. Fill out the text of the various commands to use the same convention as what is used for the Edge Impulse AT command set. For example, sending AT+RUNIMPULSE starts the inferencing process on the device. 

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Figure 1. Setting up the Edge Impulse AT Command set

Once the appropriate AT command set mapping to an icon has been done, hit the appropriate icon. Hitting the ‘play’ button cause the device to start acquiring data and perform inference every couple of seconds. The results can be viewed in the “Logs” menu as shown below.

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Figure 2. Classification Output over BLE in the Logs View

Using nRF Cloud

Using the nRF Connect for Cloud mobile app for iOS and Android, you can turn your smartphone into a BLE gateway. This allows users to easily connect their BLE NUS devices running Edge Impulse to the nRF Cloud as an easy way to send the inferencing conclusions to the cloud. It’s as easy as setting up the BLE gateway through the app, connecting to the “Edge Impulse” device and watching the same results being displayed in the “Terminal over BLE” window shown below!

Screen_Hunter_229_Feb_16_23_45_26c8913865.jpgFigure 3. Classification Output Shown in nRF Cloud

Summary

Edge Impulse is supercharging IoT with embedded machine learning and we’ve discussed a couple of ways you can easily send conclusions to either the smartphone or to the cloud by leveraging the Nordic UART Service. We look forward to seeing how you’ll leverage Edge Impulse, Nordic and BLE to create your next gen IoT application.  

 

Article originally written for the Edge Impulse blog by Zin Thein Kyaw, Senior User Success Engineer at Edge Impulse.

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By AKHILESHSINGH SAITHWAR

The LLDP protocol is a Link Layer Discovery Protocol used by network devices to identify their neighbors and their capabilities.

If you want to integrate LLDP protocol in your Linux/Embedded system, there are mainly two open-source codes. The first is lldpd and the other is openlldp. When I needed to integrate the LLDP in my network device, I studied both open-source codes. I am writing this article hoping that it will be useful for others who also want to use LLDP open-source code in their systems or network devices.

Below are the key points which should be considered when selecting the LLDP open-source code.

1. License

License is an important point to consider when you want to integrate an open-source code in your application. The lldpd is published under ISC License, whereas the openlldp is published under GPL-2.0 License. The difference between two licenses is that the ISC License is more permissive than the GPL-2.0 License.

If you use GPL-2.0 licensed open-source code in your application, you need to publish the changes back to the community. In case of ISC License, it is not required to publish your changes back to community. Please note that the scope of the article does not cover the full licensing requirements. Please understand the license before using it in your project.

2. Active Community Support

When picking up open-source code, we should also make sure that the development is active for that code. The development and support in lldpd are more active than the openlldp. When writing this article, there are a total of 8 tags in openlldp and 54 tags in lldpd. This indicates how quickly bugs are fixed and new version is released in lldpd.

3. Supported Protocols

There are other protocols like LLDP to discover the network devices, for example EDP, CDP. When selecting the LLDP open-source code, one should also make sure that it supports other protocols as well. This will make sure that the network devices with other protocols are also discovered. Though I have not verified the protocols listed in the documentations, from the document I can say that the lldpd supports EDP, CDP, FDP, SONMP and the openlldp supports EDP, CDP, EVB, MED, DCBX, VDP.

4. Custom Interface Support

In most of the cases the LLDP would run on standard Ethernet Interface but in some specific cases it may require executing LLDP on non-Ethernet interfaces, like Serial or I2C. In this case, it would be very helpful if the open-source code supports other interfaces. Though both open-source code does not support custom interfaces, the lldpd at least have documentation on how to add the custom interfaces. Adding custom interfaces on openlldp may require more time to understand and implement than lldpd.

5. Multiple Neighbour Support

This is one of the most important features when selecting the LLDP open-source code. Multiple neighbour support is needed if you are supposed to capture more than one LLDP enabled neighbour (network devices) on the same interfaces. As per my understanding, this is very basic feature which should be supported in all LLDP code. But I was surprised to know that this feature is not available in openlldp. Multiple neighbour support is available in lldpd.

6. Daemon Configuration Tool

Daemon configuration tool helps to configure the LLDP parameters, get status, enable/disable interfaces. Both lldpd and openlldp has their configuration tools. The lldpd has lldpcli/lldpctl and the openlldp has lldptool for configuration.

7. LLDP Statistics

Both lldpd and openlldp supports display of interface and neighbour statistics through there configuration tools. The statistics includes Total Frame Outs, Total Error Frame Outs, Total Age Out Frames, Total Discarded Frames, Total Frame In, Total Frame In Errors, Total Discarded Error Frames, Total TLVs in Errors, Total TLV’s Accepted etc.

8. Custom TLV Support

Both the lldpd and openlldp supports reception and transmission of custom TLV’s. The custom TLV’s can be set or get using their configuration tools.

9. SNMP Agent

Both lldpd and openlldp supports SNMP agent.

Comparison table

Based on above points the below table is populated for comparison purpose. One can decide whether lldpd or openlldp should be used in their system or network devices.

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Conclusion

As per my opinion it is better to choose the lldpd open-source code over the openlldp considering the license, features and community support. The licensing of lldpd is more permissive than the open-lldp. There are more features in lldpd compared to open-lldp. The community support for lldpd is more active than the open-lldp. So unless you have direction from your client to use specific open source lldp package, go for lldpd. eInfochips has in-depth expertise in the areas of firmware design for embedded systems development. We offer end-to-end support for firmware development starting from system requirements to testing for quality and environment.

Originally posted here.

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How IoT Tools Are Mining Manufacturing's Gold

IIoT will allow assets to perform more cost-effectively – so the better the data, the greater the savings.

Ricardo Buranello

The IoT is enabling advances across multiple market sectors, but it is the Industrial IoT (IIoT) that is having the most impact. It is already the biggest IoT vertical and covers multiple types of projects across industry, from simple data collection to more complex projects incorporating just-in-time manufacturing and predictive quality control.

The biggest benefit of the IIoT is how it is creating innovative solutions to help manufacturers achieve their business objectives by delivering better services and products to their customers. There are three principle reasons for implementing an IIoT application – to make money, to save money, or to stay compliant – and sometimes all three can be delivered. Certainly, at Telit, we would not counsel anyone to consider investing in an IIoT project unless it meets one or more of those three objectives.

Data is the New Gold

A properly implemented IIoT should enable manufacturers to collect data from every step in the process. Every machine can and should produce data, and the processing of that data should deliver invaluable information that helps create more efficient processes and factories. Look back 10-15 years, and there was a big shift in production, with manufacturing operations leaving the U.S. and Europe for China because labor cost was the most important consideration.

The IIoT is set to have the same effect as labor costs; data is the new gold. Information from the IIoT will make manufacturers’ assets perform in a more cost-effective manner – so the better the data, the greater the improvements.

Let’s look at some examples of the transformational effect of the IIoT. One of the largest car vendors in the world implemented a replacement IIoT solution that significantly reduced latency in their systems.This reduction was so relevant that in just one plant it created 3,000 minutes more of uptime. This plant produces at a rate of about $30,000 per minute, so that’s an extra $90 million.

Additionally, integrating the solution operator by operator, line by line and shift by shift, there is now a continuous link between what is being produced and how it is being produced, increasing productivity and quality control. Based on the data gathered, the manufacturer achieved significant reductions in both set-up time and line downtime.

Global names like Mitsubishi and Honda rely on the IIoT to remotely connect sophisticated machinery with technicians and engineers who constantly check manufacturing performance levels, ensure preventative maintenance, and quickly react to any issues that may affect production. Chip giants utilize the IIoT to maintain top-level cybersecurity to protect its IPR from hackers. Multinational pharmaceutical companies use the IIoT to audit every step in the manufacture of their products to ensure full compliance with regulations and laws. 

The IIoT isn’t limited to high end manufacturing. Anything can be connected. In Brazil, the IIoT is used to transmit data about the condition of the sewer network and sends alerts to maintenance crews when cleaning is required. The IIoT can also be used to explain unusual behavior.

At a manufacturing plant In Mexico, an application measuring the productivity of each machine was able to show how one machine was producing less at night than during the morning and afternoon shifts. Upon investigation, it was revealed that the operator on the evening shift was leaving the machine on a regular basis – to chat with his girlfriend.

Manufacturers are embracing the technology and investing, and without needing to hire an army of software engineers to rewrite protocols. There are experts in the IoT space that can deliver guaranteed connectivity across all systems – reducing the implementation time to a couple of days.

The IIoT is changing the face of manufacturing, from predictive maintenance and supply chain management to condition monitoring. Yet only a fraction of the market potential has been explored so far. If you look at the Fortune 500, there isn’t one company that doesn’t have an IIoT application, but in most the technology is yet to permeate the whole organization.

There are huge untapped possibilities, and work to be done to achieve the true revolution that the IIoT promises. This applies not only to the actual manufacturing processes, but throughout the supply chain, leveraging connectivity for better traceability and quality control. The IIoT can, and will, touch, impact, and improve every step.

 

Ricardo Stefanato Buranello is the Global VP - IoT Factory Solutions for Telit, and has over 14 years of experience in the M2M/IoT industry. Buranello is responsible for Telit’s global factory solutions, which is a leading provider in industrial solutions for remote connectivity, edge logic automation, OT and IT integration.

 

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by Evelyn Münster

IoT systems are complex data products: they consist of digital and physical components, networks, communications, processes, data, and artificial intelligence (AI). User interfaces (UIs) are meant to make this level of complexity understandable for the user. However, building a data product that can explain data and models to users in a way that they can understand is an unexpectedly difficult challenge. That is because data products are not your run-of-the-mill software product.

In fact, 85% of all big data and AI projects fail. Why? I can say from experience that it is not the technology but rather the design that is to blame.

So how do you create a valuable data product? The answer lies in a new type of user experience (UX) design. With data products, UX designers are confronted with several additional layers that are not usually found in conventional software products: it’s a relatively complex system, unfamiliar to most users, and comprises data and data visualization as well as AI in some cases. Last but not least, it presents an entirely different set of user problems and tasks than customary software products.

Let’s take things one step at a time. My many years in data product design have taught me that it is possible to create great data products, as long as you keep a few things in mind before you begin.

As a prelude to the UX design process, make sure you and your team answer the following nine questions:

1. Which problem does my product solve for the user?

The user must be able to understand the purpose of your data product in a matter of minutes. The assignment to the five categories of the specific tasks of data products can be helpful: actionable insights, performance feedback loop, root cause analysis, knowledge creation, and trust building.

2. What does the system look like?

Do not expect users to already know how to interpret the data properly. They need to be able to construct a fairly accurate mental model of the system behind the data.

3. What is the level of data quality?

The UI must reflect the quality of the data. A good UI leads the user to trust the product.

4. What is the user’s proficiency level in graphicacy and numeracy?

Conduct user testing to make sure that your audience will be able to read and interpret the data and visuals correctly.

5. What level of detail do I need?

Aggregated data is often too abstract to explain, or to build user trust. A good way to counter this challenge is to use details that explain things. Then again, too much detail can also be overwhelming.

6. Are we dealing with probabilities?

Probabilities are tricky and require explanations. The common practice of cutting out all uncertainties makes the UI deceptively simple – and dangerous.

7. Do we have a data visualization expert on the design team?

UX design applied to data visualization requires a special skillset that covers the entire process, from data analysis to data storytelling. It is always a good idea to have an expert on the team or, alternatively, have someone to reach out to when required.

8. How do we get user feedback?

As soon as the first prototype is ready, you should collect feedback through user testing. The prototype should present content in the most realistic and consistent way possible, especially when it comes to data and figures.

9. Can the user interface boost our marketing and sales?

If the user interface clearly communicates what the data product does and what the process is like, then it could take on a new function: sell your products.

To sum up: we must acknowledge that data products are an unexplored territory. They are not just another software product or dashboard, which is why, in order to create a valuable data product, we will need a specific strategy, new workflows, and a particular set of skills: Data UX Design.

Originally posted HERE 

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By Adam Dunkels

When you have to install thousands of IoT devices, you need to make device installation impressively fast. Here is how to do it.

Every single IoT device out there has been be installed by someone.

Installation is the activity that requires the most attention during that device’s lifetime.

This is particularly true for large scale IoT deployments.

We at Thingsquare have been involved in many IoT products and projects. Many of these have involved large scale IoT deployments with hundreds or thousands of devices per deployment site.

In this article, we look at why installation is so important for large IoT deployments – and a list of 6 installation tactics to make installation impressively fast while being highly useful:

  1. Take photos
  2. Make it easy to identify devices
  3. Record the location of every device
  4. Keep a log of who did what
  5. Develop an installation checklist, and turn it into an app
  6. Measure everything

And these tactics are useful even if you only have a handful of devices per site, but thousands or tens of thousands of devices in total.

Why Installation Tactics are Important in Large IoT Deployments

Installation is a necessary step of an IoT device’s life.

Someone – maybe your customers, your users, or a team of technicians working for you – will be responsible for the installation. The installer turns your device from a piece of hardware into a living thing: a valuable producer of information for your business.

But most of all, installation is an inevitable part of the IoT device life cycle.

The life cycle of an IoT device can be divided into four stages:

  1. Produce the device, at the factory (usually with a device programming tool).
  2. Install the device.
  3. Use the device. This is where the device generates the value that we created it for. The device may then be either re-installed at a new location, or we:
  4. Retire the device.

Two stages in the list contain the installation activity: both Install and Use.

So installation is inevitable – and important. We need to plan to deal with it.

Installation is the Most Time-Consuming Activity

Most devices should spend most of their lifetime in the Use stage of their life cycle.

But a device’s lifetime is different from the attention time that we need to spend on them.

Devices usually don’t need much attention in their Use stage. At this stage, they should mostly be sitting there and generate valuable information.

By contrast, for the people who work with the devices, most of their attention and time will be spent in the Install stage. Since those are people who’s salary you are paying for, you want to be as efficient as possible.

How To Make Installation Impressively Fast - and Useful

At Thingsquare, we have deployed thousands of devices together with our customers, and our customers have deployed many hundreds of thousands of devices with their customers.

These are our top six tactics to make installation fast – and useful:

1. Take Photos

After installation, you will need to maintain and troubleshoot the system. This is a normal part of the Use stage.

Photos are a goldmine of information. Particularly if it is difficult to get to the location afterward.

Make sure you take plenty of photos of each device as they are installed. In fact, you should include multiple photos in your installation checklist – more about this below.

We have been involved in several deployments where we have needed to remotely troubleshoot installations after they were installed. Having a bunch of photos of how and where the devices were installed helps tremendously.

The photos don’t need to be great. Having a low-quality photo beats having no photo, every time.

 

2. Make it Easy to Identify Devices

When dealing with hundreds of devices, you need to make sure that you know exactly which you installed, and where.

You therefore need to make it easy to identify each device. Device identification can be made in several ways, and we recommend you to use more than one way to identify the devices. This will reduce the risk of manual errors.

The two ways we typically use are:

  • A printed unique ID number on the device, which you can take a photo of
  • Automatic secure device identification via Bluetooth – this is something the Thingsquare IoT platform supports out of the box

Being certain about where devices were installed will make maintenance and troubleshooting much easier – particularly if it is difficult to visit the installation site.

3. Record the Location of Every Device

When devices are installed, make sure to record their location.

The easiest way to do this is to take the GPS coordinates of the devices as it is being deployed. Preferably with the installation app, which can do this automatically – see below.

For indoor installations, exact GPS locations may be unreliable. But even for those devices, having a coarse-grained GPS location is useful.

The location is useful both when analyzing the data that the devices produce, and when troubleshooting problems in the network.

 

4. Keep a Log of Who Did What

In large deployments, there will be many people involved.

Being able to trace the installation actions, as well as who took what action, is enormously useful. Sometimes just knowing the steps that were taken when installing each device is important. And sometimes you need to talk to the person who did the installation.

5. Develop an Installation Checklist - and Turn it into an App

Determine what steps are needed to install each device, and develop a step-by-step checklist for each step.

Then turn this checklist into an app that installation personnel can run on their own phones.

Each step of each checklist should be really easy understand to avoid mistakes along the way. And it should be easy to go back and forth in the steps, if needed.

Ideally, the app should run on both Android and iOS, because you would like everyone to be able to use it on their own phones.

Here is an example checklist, that we developed for a sensor device in a retail IoT deployment:

  • Check that sensor has battery installed
  • Attach sensor to appliance
  • Make sure that the sensor is online
  • Check that the sensor has a strong signal
  • Check that the GPS location is correct
  • Move hand in front of sensor, to make sure sensor correctly detects movement
  • Be still, to make sure sensor correctly detects no movement
  • Enter description of sensor placement (e.g. “on top of the appliance”)
  • Enter description of appliance
  • Take a photo of the sensor
  • Take a photo of the appliance
  • Take a photo of the appliance and the two beside it
  • Take a photo of the appliance and the four beside it
 

6. Measure Everything

Since installation costs money, we want it to be efficient.

And the best way to make a process more efficient is to measure it, and then improve it.

Since we have an installation checklist app, measuring installation time is easy – just build it into the app.

Once we know how much time each step in the installation process needs, we are ready to revise the process and improve it. We should focus on the most time-consuming step first and measure the successive improvements to make sure we get the most bang for the buck.

Conclusions

Every IoT device needs to be installed and making the installation process efficient saves us attention time for everyone involved – and ultimately money.

At Thingsquare, we have deployed thousands of devices together with our customers, and our customers have deployed many hundreds of thousands of devices with their customers.

We use our experience to solve hard problems in the IoT space, such as how to best install large IoT systems – get in touch with us to learn more!

Originally posted here.

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“Productivity isn’t everything, but in the long run, it is almost everything.” This well-known quote is attributed to Paul Krugman, the well-known American economist and winner of a Nobel Memorial Prize in Economic Sciences for his contributions to New Trade Theory and New Economic Geography.

In economic terms, a common definition of productivity cites it as the ratio between the volume of outputs and the volume of inputs. It measures the efficiency of production inputs – labor and capital – used to produce a given level of output.

 
For countries and companies alike, productivity gain is a fundamental goal. For countries, productivity leads to higher real income, which contributes to higher living standards and better social services.

For companies, productivity is a key driver of sustainable profits and competitiveness over time. The global economy, with open markets and wide competition, pushes companies for constant productivity gains. Companies that fail in the race for productivity are the perfect candidates for extinction in the near future.

 

Productivity can be boosted in a few different ways, most notably through the innovation of new products or through new business models that guarantee higher scalability and demand. One example is how Starbucks built a sustainable business model with high levels of productivity through the deployment of strong, intangible assets such as a unique brand and efficient business processes.

Another example is Apple, a company that executed its strategy to perfection, creating a legion of fans that constantly run to buy the company’s new products, and sometimes even camp overnight outside an Apple store to get a device before it sells out. Apple succeeded not only in designing some of the most desired smartphones and PCs on the market but also in creating a business platform that generates incremental service and software revenue on top of its products. In 2020, about 15% of Apple’s revenue came from services, leveraged by its platform strategy.

Another important factor in productivity is the innovation inside. That is, how to produce more with fewer resources. While in the past few decades industrial efficiency was boosted by moving factories to low labor cost economies, this recipe is getting exhausted. The cost increase in Asian countries, driven by higher salaries, geopolitical risks and the increase in automation levels is changing the balance of this equation.

In an environment of hyper-competition and open markets, technology is rapidly reshaping manufacturing. The companies that survive in this new paradigm will be those that adopt data-driven models, innovate on their products and services, and embrace the challenge of producing more with less. I believe IoT and Industry 4.0 will be the drivers of this transformation.

Start With Management

Everything starts with management. Managers need to embrace innovation and constant improvement. Processes need to be quantified, and efficiency ratios for each of the individual processes need to be measured. For example, overall equipment effectiveness (OEE) needs to be calculated per machine, line, operator, sector and plant. Such KPIs are important to enable managers to make real-time decisions.

Include Machines

If data-driven management is the goal, then it’s time to think about execution. The ability to collect data from a variety of different machines and from a variety of different vendors is a big challenge. Industrial machines in general don’t have a common protocol and as such, collecting the data in a highly efficient manner can be challenging and daunting.

Beyond connecting machines themselves, machine data needs to be efficiently integrated across different IT systems and software, such as manufacturing execution systems (MES), enterprise resource planning (ERP) software and a variety of database applications. On top of that, there comes the challenge of building and integrating higher-level functionality, such as edge logic for real-time actions, data visualization for operators and managers, data analytics, cloud computing, machine learning and the list goes on. The complexity and associated challenges of machine and data integration cause many companies to fail along the way.

Avoid The Custom Code Trap

Many companies fail in the execution, and one of the reasons is because it is not a simple task. As IIoT is a relatively new concept, the market is not fully matured. Many companies create their own internal team and start to code. The problem is companies may not be prepared – they often lack the right level of skills, people, and expertise. It's not impossible to execute internally, but oftentimes focusing on your core business and finding the best technology tools for your needs in the market is the more efficient choice.

If you're looking at outside teams, a good way to avoid high development costs and operations risk is to find an integrated platform that merges data collection, edge computing and information technology/operational technology (IT/OT) integration. The more vertically integrated, the faster the deployment and the less likely you will need "Band-Aids" to integrate systems. This will provide more flexibility and optimize performance while reducing the cost and risks of the project.

It’s also important to remember that innovation and productivity is more than a task. It is a journey. Processes need to constantly evolve, and your IIoT platform must provide the ability to be flexible when you need to change machines, systems, metrics and processes.

In the end, productivity excellence is a blend of management, creativity and technology. It means pushing people out of their comfort zone and augmenting possibilities with technology. Not easy, but certainly needed.

 

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